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Heat generated by electronic devices can lead to thermal deformation, damage, and fatigue failure, underscoring the importance of monitoring heat distribution. This study introduces an artificial neural network using two thermocouples for cost-effective temperature distribution prediction. Experimental data from heated systems on chip with attached heat sinks were used for training and validation, integrating thermocouple measurements and infrared camera data. The method’s applicability was verified across four different heat sinks. Additionally, finite element analysis compared stress and strain based on predicted and actual temperature distributions, addressing conventional limitations that focus solely on temperature validation. Furthermore, the temperature of any coordinate could be output by including the coordinate as an input of neural networks, eliminating the hassle of re-constructing or learning the DNN to obtain the temperature of the desired coordinate. Results showed that the temperature distribution could be predicted with high accuracy (over 0.95), and the maximum error rate for stress and strain predictions was 7% in the worst case. This confirmed the feasibility of artificial neural networks to predict the temperature distribution using a minimal number of sensors and ensure robust performance even if the heat sink changes.

In South Korea, the rapid proliferation of smartphones has led to an uptick in messenger phishing attacks associated with electronic communication financial scams. In response to this, various phishing detection algorithms have been proposed. However, collecting messenger phishing data poses challenges due to concerns about its potential use in criminal activities. Consequently, a Korean phishing dataset can be composed of imbalanced data, where the number of general messages might outnumber the phishing ones. This class imbalance problem and data scarcity can lead to overfitting issues, making it difficult to achieve high performance. To solve this problem, this paper proposes a phishing messages classification method using Biased Discriminant Analysis without resorting to data augmentation techniques. In this paper, by optimizing the parameters for BDA, we achieved exceptionally high performances in the phishing messages classification experiment, with 95.45% for Recall and 96.85% for the BA metric. Moreover, when compared with other algorithms, the proposed method demonstrated robustness against overfitting due to the class imbalance problem and exhibited minimal performance disparity between training and testing datasets.

A workflow model specifies execution sequences of the associated activities and their affiliated relationships with roles, performers, invoked-applications, and relevant data. These affiliated relationships exhibit a series of valuable human-centered organizational knowledge and are utilized for exploring human resource’s work patterns. This paper focuses not only on a specific type of affiliated relationships between performers and activities, in particular, which forms a performer-to-activity affiliation network, but also on a specific type of analysis techniques, which builds a closeness centrality measurement approach for quantifying the degrees of farnesses between performers as well as between activities. In other words, this paper investigates a series of formal approaches for building organizational closeness centrality measurement techniques on the specific type of affiliation networks. The investigation mainly deploys two types of algorithmic formalisms along with an operational example, which are measuring performer-centered organizational closeness centralities and activity-centered organizational closeness centralities, respectively. In order to validate the deployed algorithmic equations, the paper carries out a couple of operational experiments; One is on an ICN-based workflow package and the other is on a discovered workflow model mined from a dataset of workflow event logs. Summarily, this paper devises a series of algorithms and equations for measuring closeness centralities of activities, verify the devised algorithms and their related equations along with operational examples, and discuss the ultimate implications of these analysis techniques of organizational closeness centrality measurements as the performer-to-activity affiliation networking knowledge in workflow-supported organizations.

Process mining that exploits system event logs provides significant information regarding operating events in an organization. By discovering process models and analyzing social network metrics created throughout the operation of the information system, we can better understand the roles of performers and characteristics of activities, and more easily predict what will occur in the next operation of a system. By using accurate and valuable predicted information, we can create effective environments, provide suitable materials to perform activities better, and facilitate more efficient operations. In this study, we apply the long short-term memory, a variant of the recurrent neural network, to predict the enterprise social networks that are formed through information regarding a business system’s operation. More precisely, we apply the multivariate multi-step long short-term memory model to predict not only the next activity and next performer, but also all the variants of a process-aware enterprise social network based on the next performer predictions using a probability threshold. Furthermore, we conduct an experimental evaluation on the real-life event logs and compare our results with some related researches. The results indicate that our approach creates a useful model to predict an enterprise social network and provides metrics to improve the operation of an information system based on the predicted information.

Abstract Little is known about changes in the abundance of tumor-infiltrating lymphocytes (TILs) and immune phenotype (IP) in recurrent and/or metastatic (R/M) head and neck squamous cell carcinoma (HNSCC). We aimed to compare the TILs and IP between initial and recurrent HNSCCs using paired analysis. Thirty-seven patients who experienced recurrence after surgical resection and received treatment with immune checkpoint inhibitors (ICIs) between June 2014 and June 2023 were included. Changes in intratumoral TIL (iTILs), stromal TIL (sTILs), and IPs were subjected to paired analysis between the initial and recurrent tumors. We investigated their relationship with the outcomes of ICIs. The density of iTIL and sTIL in the recurrent tumors was significantly lower compared to initial tumors. IP was significantly different; the proportion of desert IP was higher in recurrent tumors (83.8% vs. 35.1%, P

As the penetration of electric vehicles (EVs) accelerates according to eco-friendly policies, the impact of electric vehicle charging demand on a power distribution network is becoming significant for reliable power system operation. In this regard, accurate power demand or load forecasting is of great help not only for unit commitment problem considering demand response but also for long-term power system operation and planning. In this paper, we present a forecasting model of EV charging station load based on long short-term memory (LSTM). Besides, to improve the forecasting accuracy, we devise an imputation method for handling missing values in EV charging data. For the verification of the forecasting model and our imputation approach, performance comparison with several imputation techniques is conducted. The experimental results show that our imputation approach achieves significant improvements in forecasting accuracy on data with a high missing rate. In particular, compared to a strategy without applying imputation, the proposed imputation method results in reduced forecasting errors of up to 9.8%.

Work transference network is a type of enterprise social network centered on the interactions among performers participating in the workflow processes. It is thought that the work transference networks hidden in workflow enactment histories are able to denote not only the structure of the enterprise social network among performers but also imply the degrees of relevancy and intensity between them. The purpose of this paper is to devise a framework that can discover and analyze work transference networks from workflow enactment event logs. The framework includes a series of conceptual definitions to formally describe the overall procedure of the network discovery. To support this conceptual framework, we implement a system that provides functionalities for the discovery, analysis and visualization steps. As a sanity check for the framework, we carry out a mining experiment on a dataset of real-life event logs by using the implemented system. The experiment results show that the framework is valid in discovering transference networks correctly and providing primitive knowledge pertaining to the discovered networks. Finally, we expect that the analytics of the work transference network facilitates assessing the workflow fidelity in human resource planning and its observed performance, and eventually enhances the workflow process from the organizational aspect.

Introduction: We discuss event-related power differences (ERPDs) in low- and broadband-γ oscillations as the embedded-clause edge is processed in wh-dependencies such as Which decision regarding/about him/her did Paul say that Lydie rejected without hesitation? in first (L1) and second language (L2) French speakers. Methods: The experimental conditions manipulated whether pronouns appeared in modifiers (Mods; regarding him/her) or in noun complements (Comps; about him/ her) and whether they matched or mismatched a matrix-clause subject in gender. Results: Across L1 and L2 speakers, we found that anaphora-linked ERPDs for Mods vs. Comps in evoked power first arose in low γ and then in broadband γ. Referential elements first seem to be retrieved from working memory by narrowband processes in low γ and then referential identification seems to be computed in broadband-γ output. Interactions between discourse- and syntax-based referential processes for the Mods vs. Comps in these ERPDs furthermore suggest that multidomain γ-band processing enables a range of elementary operations for discourse and semantic interpretation. Discussion: We argue that a multidomain mechanism enabling operations conditioned by the syntactic and semantic nature of the elements processed interacts with local brain microcircuits representing features and feature sets that have been established in L1 or L2 acquisition, accounting for a single language epistemology across learning contexts. [ABSTRACT FROM AUTHOR]

With current plans for manned missions to Mars and beyond, the need to better understand, prevent, and counteract the harmful effects of long-duration spaceflight on the body is becoming increasingly important. In this study, an automated heart-on-a-chip platform was flown to the International Space Station on a 1-mo mission during which contractile cardiac function was monitored in real-time. Upon return to Earth, engineered human heart tissues (EHTs) were further analyzed with ultrastructural imaging and RNA sequencing to investigate the impact of prolonged microgravity on cardiomyocyte function and health. Spaceflight EHTs exhibited significantly reduced twitch forces, increased incidences of arrhythmias, and increased signs of sarcomere disruption and mitochondrial damage. Transcriptomic analyses showed an up-regulation of genes and pathways associated with metabolic disorders, heart failure, oxidative stress, and inflammation, while genes related to contractility and calcium signaling showed significant down-regulation. Finally, in silico modeling revealed a potential link between oxidative stress and mitochondrial dysfunction that corresponded with RNA sequencing results. This represents an in vitro model to faithfully reproduce the adverse effects of spaceflight on three-dimensional (3D)-engineered heart tissue. [ABSTRACT FROM AUTHOR]

Purpose: For tonsillar cancer with multiple ipsilateral neck lymph nodes, the safety and efficacy of unilateral radiotherapy (RT) have long been a topic of debate. We performed retrospective analyses of patients having ipsilateral neck lymph nodes treated with unilateral RT in two tertiary referral hospitals. Materials and Methods: This study accrued 29 patients who were diagnosed as well-lateralized tonsillar cancer with multiple ipsilateral neck lymph nodes and underwent unilateral RT from March 2000 to March 2020. Patients underwent treatment with one of the following options or a combination of them: induction chemotherapy, surgery, RT, and concurrent chemoradiotherapy. We analyzed the recurrence pattern and survival with special attention to contralateral neck failure. Also, treatment- related toxicities were compared with a 1:1 matched cohort of those who received bilateral RT, using propensity score matching analysis. Results: At a median follow-up of 68 months, no contralateral neck failure was observed. Five-year actuarial locoregional recurrence-free survival, distant metastasis-free survival, and overall survival were 85.6%, 91.8%, and 92.7%, respectively. Both the acute and chronic grade 2 xerostomia occurred in 10.3% of the patients. When the toxicity for unilateral RT was compared to that of bilateral RT using a propensity score-matched cohort, a significantly lower rate of acute xerostomia was observed in unilateral RT group (55.1% vs. 82.7%, p=0.002), primarily at grade 2 level (10.3% vs. 51.7%, respectively) Conclusion: The results of our study suggest that unilateral RT can be safely performed in well-lateralized tonsillar cancer patients with multiple ipsilateral neck lymph nodes. [ABSTRACT FROM AUTHOR]

We investigated assessments of calorie production and energy efficiencies of phytoplankton in Gwangyang, Jaran, and Geoje-Hansan bays in southern Korea, based on seasonal field measurements data for particulate organic carbon (POC), macromolecular compositions, calorie contents, and primary production. Our findings revealed that Geoje-Hansan Bay consistently exhibits higher POC concentrations compared to Gwangyang and Jaran bays, except during the summer season. The observed seasonal variations in POC concentrations and primary productions indicated distinct factors influencing POC distribution among the bays, which varies with the seasons. Macromolecular compositions displayed notable patterns, with Gwangyang Bay exhibiting pronounced seasonal variations, Jaran and Geoje-Hansan bays showing stable carbohydrate (CHO) dominance, and Gwangyang Bay displaying protein (PRT) dominance influenced by river-borne nutrients. Analysis of calorie content revealed that Geoje-Hansan Bay exhibited the highest calorie content, suggesting a more favorable physiological state of phytoplankton compared to Gwangyang and Jaran bays. Estimations of annual primary production and calorie production demonstrated regional variations, with Geoje-Hansan Bay having the highest values followed by Jaran Bay and Gwangyang Bay. These differences in primary production and calorie production reflect the diverse ecological conditions and nutrient availability specific to each bay. Interestingly, Gwangyang Bay demonstrated the highest energy efficiency, producing more calories per unit of carbon compared to the other two bays, potentially due to the influence of different dominant phytoplankton communities. This study enhances our understanding of ecosystem dynamics and ecological characteristics among the three bays, emphasizing the importance of considering seasonal variations and specific bay characteristics in investigating biogeochemical processes, energy flow, and ecosystem functioning. The findings contribute valuable insights for the sustainable management of coastal ecosystems and aquaculture practices. [ABSTRACT FROM AUTHOR]

Introduction: Male breast cancer (MBC) is a rare condition, and recent research has underscored notable distinctions between MBC and breast cancer in women. This study aimed to assess and contrast the long-term survival outcomes and disease patterns of MBC patients with those of their female counterparts. Methods: We analyzed data from 113,845 patients diagnosed with breast cancer who had undergone curative surgery from the Korean Breast Cancer Registry (KBCR) between January 1990 and August 2014 in Seoul, Korea. The five-year overall survival was analyzed according to clinicopathological characteristics. Results: Among 113,845 patients with breast cancer, 473 MBC cases were included. The median duration of follow-up was 72 months. The median age at diagnosis was 60 and 48 years for MBC and female breast cancer, respectively. Most male patients (92.6%) underwent total mastectomy, while 50.4% of female patients underwent breast-conserving surgery. Among MBC, 63.2% received chemotherapy, and 83.9% of hormone receptor-positive male patients received endocrine therapy. In survival analysis, MBC demonstrated distinct 5-year overall survival patterns compared with female breast cancer, according to age at diagnosis. In women with breast cancer, the younger age group (=40 years) demonstrated worse 5-year overall survival than did the older age group (>40 years) (91.3% vs 92.7%, p <0.05). While in MBC, the younger age group (=40 years) demonstrated better 5-year overall survival than did the older age group (>40 years) (97.4% vs 86.4%, p <0.05). Discussion: In conclusion within this extensive cohort, we have revealed unique survival patterns in MBC that diverge from those observed in women with breast cancer. This study enhances our comprehension of MBC prognosis and can potentially shed light on unresolved questions, paving the way for future research in the realm of MBC. [ABSTRACT FROM AUTHOR]